In a medical field, for example, image diagnosis using radiographs and the like are used. Conventionally, the image diagnosis requires a doctor to visually check diagnostic images piece by piece in order to determine whether there is abnormality or not. As such, if a conventional image diagnosis is followed, then it is clear that heavier workload is imposed on a doctor.
Recently, studies have been actively conducted in an effort to build a neural network modeling a human brain function and use it in a field of image diagnosis. For example, Patent Literature 1 discloses a novel and excellent method and system for detecting a micro-calcified substance by inspection of a digital breast radiograph.
In the method of Patent Literature 1 and the like, an abnormal region (region of interest) in a digital chest radiograph, which corresponds to an organ/tissue suspected of having a micro-calcified substance, is extracted at first. Then, the region of interest is converted into numerical data, which are then inputted to a neural network learned to detect a micro-calcified substance. In response, a result of detecting and the region of interest are outputted from the neural network.
The micro-calcified substance detecting method etc. extracts the regions of interest in advance, thereby making it possible to attain a decrease in a number of false positive regions, while keeping all true positive regions.